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Field
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Additional Information Eligibility criteria Training and experience: A PhD in Materials Science, Computational Chemistry, Physics, or a related field, with a strong background in DFT modeling and experience in
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with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental design
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modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
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as a one‐year appointment, but renewable annually based on performance. The position involves postdoctoral work in developing efficient methods and tools for analyzing large-scale biomedical data with
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learning PREFERRED QUALIFICATIONS: Experience using computational methods to analyze large-scale high-dimensional biomedical data relating to clinical information, genetics, genomics, radiomics, and/or
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machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks to minutes thus
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data science, and/or public health or related fields including health services research, health informatics, computer scienceExperience in data analysis using statistical software & machine learning (e.g
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that integrate multi-omics data to uncover mechanisms of disease, cellular resilience, and therapeutic response. The post holder will lead research applying large-scale machine learning and foundation models
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that combines modern machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks
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of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in design and very good programming skills (Python, Pytorch